Enni Technologies is an AI-native company. We build and research applied intelligence — LLM systems, agents, and on-device AI — designed to hold up in production, not just demo well.
We take a small number of hard problems and go deep, building systems that stay reliable once real users and real data arrive.
Retrieval, knowledge assistants, and structured generation that stay grounded in your data — measured, bounded, and cost-aware rather than impressive once and unreliable after.
Agentic systems that take real actions inside real constraints — bounded loops, tool use, and evaluation built in from the first line, so autonomy never means unpredictability.
Models that run where the data lives — fast, private, and offline-capable. For products where latency, cost, or confidentiality rule out a round trip to someone else's cloud.
The foundational decisions, made well — model strategy, evaluation design, and system architecture so a serious AI build rests on the right footing from the start.
We don't just apply intelligence — we investigate it.
Research isn't a separate department here. The open questions we work on feed directly into what we build, and what we ship sends us back to the questions.
Running capable models inside tight latency, cost, and privacy budgets — on device and at the edge.
Keeping generated output anchored to verifiable sources, and knowing when a system should say "I don't know."
Making autonomous systems measurable and predictable — evaluation, failure modes, and bounded behavior.
Apple's new on-device flagship keeps 20B parameters resident but activates only a few billion per prompt. Why it's pinned to the A19 Pro — and what it means for shipping real AI to the devices people already own.
Read →A from-scratch PyTorch reproduction of the technique from Apple's foundation-model paper — and the 37.5% KV-cache memory reduction that matched it exactly.
In progressNo black box. You see every turn, and each cycle's result becomes the next cycle's input — until the criteria are met.
The problem, the data, and whatever the last cycle returned. Context before action — always.
The smallest step that cuts the most uncertainty — which model, which test, which slice to build next.
Every turn ends in something executable you can open, not a status update.
Quality, cost, latency, failure modes — against the criteria. The result isn't an endpoint; it's the next iteration's input.
Enni Technologies is an independent, AI-native company.
We build applied intelligence — systems that reason, retrieve, and act — and we research the problems underneath them, taking on few at a time so each gets full attention.
No growth for its own sake, no buzzwords standing in for results. Just AI engineering and research held to a standard you can measure.